In order to test the effectiveness of the RCA framework, we explore a comprehensive multi-organ segmentation task on whole-body MRI. In this application, we evaluate the prediction accuracy of RCA in the context of three different state-of-the-art segmentation methods, a Random Forest approach [1]}, a deep learning approach using 3D CNNs [2]}, and a probabilistic multi-atlas label propagation approach [3]}. The dataset used to validate our framework is from our MALIBO (MAchine Learning In whole Body Oncology) study. We collected whole-body, multi-sequence MRI (T1w Dixon and T2w images) of 35 healthy volunteers. Detailed manual segmentations of 15 anatomical structures, including abdominal organs (heart, left/right lung, liver, adrenal gland, gall bladder, left/right kidney, spleen, pancreas, bladder) and bones (spine, left/right clavicle, pelvis) have been generated by clinical experts as part of the study. These manual segmentations will serve as GT in the quantitative evaluation.
